similar to: Estimating mixed logit using Maximum simulated likelihood

I'm trying to estimate the parameters for GARCH(1,1) process. Here's my code: loglikelihood <-function(theta) { h=((r[1]-theta[1])^2) p=0 for (t in 2:length(r)) { h=c(h,theta[2]+theta[3]*((r[t-1]-theta[1])^2)+theta[4]*h[t-1]) p=c(p,dnorm(r[t],theta[1],sqrt(h[t]),log=TRUE)) } -sum(p) } Then I use nlminb to minimize the function loglikelihood: nlminb(

Dear all, I need to simulate data which fit to a double poisson time series model with certain parameters. Then, check whether the estimated parameter close to the true parameter by using maximum likelihood estimation. Simulation: set.seed(10) library("rmutil") a0 = 1.5; a1 = 0.4; b1 = 0.3; g1= 0.7 ; n=100 #a0, a1 and b1 are parameter where n is size. nu = h =

i'm not sure why i work so hard to be accurate and fair when i'm discussing jim, when he in turn seems happy to engage in innuendo and outright _lies_ to slime me. some of the things he says are flat out 360-degree lies, so he obviously cares not even a whit for his credibility. which is why i feel no need any more to counter his crap. because once you've cleared away his

Hi, I just ran the following logit regression. But can anyone tell me how to calculate how much more likely males (Male=1) could show such symptom than females(Male=0)? I know it must be simple to get once I have the coefficients, but I just don't recall. Thank you very much! Call: glm(formula = Symptoms ~ 1 + Male, family = binomial(link = logit), data = HA) Deviance Residuals:

Hi all -- I have to calculate a likelihood ratio test for a logit model. I found logLik, but I need to calculate the log likelihood for the model without any predictors. How can I specify this in glm? If the full model is glm(y ~ x1), is the one without predictors (y ~ 0)? Or (y ~ 1)? Is there a more direct way of getting this? -- Chris

Is there an equivalent to MPlus's Full Information Maximum Likelihood (FIML) missing data estimator for R? If so, is there a way to take covariance structures produced by such a package and perform multiple regression with these? If you are unfamiliar with Mplus' FIML below is a link to their manual. Their estimation technology is discussed on page 25. I have asked the developer of the

Hello, I'm trying to calculate the Maximum likelihood estimators for a dataset which contains censored data. I started by using the function "nlm", but isn't there a separate method for doing this for e.g. the "weibull" and the "log-normal" distribution? Thanks, Olivia [[alternative HTML version deleted]]

Have a look at ?optim. I don't think it has the BHHH algorithm as an option, though. =========================================== David Barron Jesus College University of Oxford -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch]On Behalf Of Harold Doran Sent: 10 July 2003 15:43 To: Fohr, Marc [AM]; R-help at stat.math.ethz.ch

Hi, I have a quick question regarding estimation of a truncation regression model (truncated above at 1) using MLE in R. I will be most grateful to you if you can help me out. The model is linear and the relationship is "dhat = bhat0+Z*bhat+e", where dhat is the dependent variable >0 and upper truncated at 1; bhat0 is the intercept; Z is the independent variable and is a uniform

Dear all, I posted this two years ago, getting no answers or suggestions - now I am trying again, hoping something new is available in R. I am interested in an application of linear multilevel model with unequal selection probabilities at both levels. Do you know if there is an R function for multilevel pseudo-maximum likelihood estimation? Or is it possible to obtain these estimates using

r-help at stat.math.ethz.ch on Saturday, May 13, 2006 at 6:00 AM -0500 wrote: >>One of my friends recently wrote his PhD thesis from University of >>Leeds under Kanti Mardia's direction. I bet your friend was really angling for that. -- Alan B. Cobo-Lewis, Ph.D. (207) 581-3840 tel Department of Psychology (207) 581-6128 fax University of Maine Orono, ME 04469-5742 alanc

Dear List: Is any one aware of a package that would extend the D() function and allow for one to maximize a likelihood function symbolically? Something akin to Solve[x==0, parameter] function in Mathematica? Clearly R has the capacity to _compute_ MLEs given a set of data. But, I'm looking for a package that would allow for me to define the likelihood function, find the 1st order partial

This is to announce a new package MLCM on CRAN. The package provides functions for estimating perceptual scales by maximum likelihood from data collected in a conjoint measurement experiment. Data for conjoint measurement are typically collected using a psychophysical procedure. The stimuli vary along 2 or more dimensions. The observer views pairs of stimuli and judges which stimulus of each pair

This is to announce a new package MLCM on CRAN. The package provides functions for estimating perceptual scales by maximum likelihood from data collected in a conjoint measurement experiment. Data for conjoint measurement are typically collected using a psychophysical procedure. The stimuli vary along 2 or more dimensions. The observer views pairs of stimuli and judges which stimulus of each pair

hi, using articial data, i'm supposed to estimate model y(t) = beta(1) + beta(2)*x(t) + u(t), u(t) = gamma*u(t-1) + v(t), t = 1,...,100 which is correctly specified in two ways: ML ommiting the first observation, and ML using all 100 observation. since i'm still learning how to use R, i would like to know how MLE works. there is neither information about the distribution of v(t) nor

Dear R users >From what I understand, the joint maximum likelihood procedure for Rasch (availabe in the package MiscPsycho) in R can only be used on binary data. I was wondering if the code is currently being adapted for application to ordinal data? I'm trying to replicate results obtained from Winsteps in R. Best wishes denn -- View this message in context:

Dear R - users/Helpers I am dealing with bivariate Normal data with missing values. Further I am trying to implement Expectation-Maximization (EM) algorithm to resolve the missing data problem. Now one of the requirements is use the Log likehood function i.e -2Log so as to find a reliable convergence.... My question is there any R built function for the same or do i have to use the

Hi! Recently I try to find the method maximum likelihood for gamma,weibull,Pearson type III,Kappa Distribution, mixed exponential distribution, skew distribution. I have tried function ms() for gamma two parameters and weibull two parameters.It works but not for Pearson type III. I have problem to find the likelihood function for mixed exponential distribution and kappa distribution. So can

Hello all, I'm trying to calculate the Maximum likelihood of individuals to get the ancestry. I mixd 3 populations 15 generations in proportion of 20% 20% 60% when each population sorce have diferent genome (0 1 and 2) with frequencies for each one. So now i have individuals looks like 0 0 2 1 1 2 0 ..... and i don't now how to calculate the mle although i try to figure out from the

Hi, I want to use Maximum likelihood to estimate the parameters from my regression line. I have purchased the book "Applied linear statistical models" from Neter, Kutner, nachtsheim & Wasserman, and in one of the first chapters, they use maximum likelihood to estimate the parameters. Now I want to tried it for my self, but couldn't find the right function. In the book, they give